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The Heart Drum

Publishing one of my old free-writings as I’m picking back up my fictional writing. Note: This work has nothing to do with my dissertation.


purpleheartI beat and you’re confused. I drum the beats and your superiors are doubtfulness, laziness. I beat and drum and even more drum. My rhythm of quakes is forever and steadfast. It knows not the foolish playfulness. It mixes not inside the clay of the desires, shapes of vacuum of hopes. In the playground of the puppets, the whirling sounds and waves of the drums remain distanced, unfamiliar.

The ropes to the dangling rides cannot hold much longer. What will they do when the rope breaks? Gravity takes over… and they will fall. No doubt there. How will they refrain from catching the cold of the clay onto their celestial bodies and minds? The clay, the sands and the waters that is, is waiting. Just the glimpse of their fall is amusing enough… it is worth the wait.

The ropes of the dangling rides are pulled and squeezed; the actions and the reactions to them. How much more pull can you take? Where is the point of the breakdown? Is it not soon? Will it not happen any moment? How about the law of the probabilities? The kings of the playgrounds know this rule and fear the fall, and the splash, and the covered up bodies in the clay. Quietly working in the background, they know the rules and they follow it; hsssshhh… make no sound, remain invisible, stay still… and wait. They will fall any moment, this is their promise to the muds. Their consensual agreement is laudable. The pray and the haunter are both one and the same.

The rulers, the kings, once were bodies too, you know. They, too, joyed the playground. The rope and the dangling rides were their only companions too, you see. At the end? They fell. They fell down into the mud. The bodies of the minds mingled with the clays and became one. Nowhere else to go… and they had nothing more to say and no more games to play. Their world became the rules of the playground. The only mission turned into the darkest one: to suck as many bodies as we can aim.

The heartbeat, the kind one, the one with tears of the eyes and the compassions of the chests could not watch. The pain got unbearable. Shall I let go of you? I can’t watch the tearing of the blood veins that go right through me. My life is their life. So, how should I let go? I love you too much. You should know: the world of the muds is not where you were destined to be.

The creation of your veins and minds was for the greater purpose than playgrounds and clays. So, how dare you to obey the invitations of the pots? The fiesta of promises is empty with no base. So, stay with me, get nourished in me, sing to me…. keep still… let me hold you, hug you, kiss you. Feel the rush of the love-cells in your veins and see your blood turn into wine… and then hold your cup and just drink. Let me love you, hold you, forever and ever, my only love.

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A Position Paper at the iConference 2015

I am excited to have been invited to present a position paper on my doctoral work at the upcoming iConference, presented by the iSchools association. The conference focuses on research work by information science scholars worldwide and will be hosted by The Donald Bren School of Information and Computer Sciences at the University of California, Irvine. So, for those interested, I thought of sharing part of this paper here. And if you are planning on attending the conference, please come by the conference workshop!


Web

1        Introduction

The affective component of information retrieval system design is becoming increasingly essential within the field of information retrieval, as it encompasses complicated human cognitive processes. Cognitive processes include not only mental processes but also emotion (or affective) processes and responses (Picard, 2001). Therefore, it is important to move toward understanding the affective dimension of information retrieval. The goal of this study is to explore and examine the neurological affective components of information retrieval systems, more specifically in web search processes and search performance.

Over the past decade, information retrieval research studies have evolved and become increasingly sophisticated. System-oriented approach was one of the first types of studies where information retrieval systems were the focal point of attention. However, researchers began to realize that not only do we need to examine machines but we also need to study user interaction with the systems. This, in turn, led to user-oriented approach. Shortly thereafter, researchers began to detect sophisticated cognitive processes when dealing with information retrieval systems. As a result, studies began to turn to cognitive-oriented approaches.

Most recently, research communities are detecting how human emotions may play a significant role in human-computer-interaction. Expressions such as “pleasurable engineering” or “emotional design” have become the driving factors in system design, and these expressions have also been extended to information retrieval system design (Nahl & Bilal, 2007). These emerging factors and expressions indicate the important role of emotions in human-computer-interaction, highlighting the importance of including the affective dimensions when designing information retrieval systems. However, our understanding of how emotions affect search processes, as revealed in search performance—search effectiveness and search efficiency—is limited (Nahl & Bilal, 2007).

As a result, the emotion-oriented approach has risen to the surface, making researchers realize the potential effects of affective dimensions on user information retrieval processes. More specifically, researchers are increasingly exploring the neurological aspects of cognitive and emotion responses. This research intends to contribute to the emotion-oriented approach studies, aiming to add value to the evolution of information retrieval research approaches by further exploring neuro-information science.

2        Problem Statement and Research Questions

There is a gap in the current body of knowledge on the effects of physiological and neurological emotion responses in information retrieval, more specifically on web search. This pilot study aimed to examine the effect of different dimensions of emotions on web search performance, as revealed in search efficiency and search effectiveness.

  • Q1: How do dimensions of emotions affect search effectiveness?
  • Q2: How do dimensions of emotions affect search efficiency?
  • Q3: Are there any interactional effects between dimensions of emotions and search performance?

The hypothesis is that positive emotional states have positive effects on information retrieval and negative emotional states affect users’ web search performance negatively.

3        Industry implications

In an era when humans are creating brain controlled airplanes, neuro-gaming, and robots that learn behavior by reading human emotions, there appear to be no limits in having search engines read human emotions in order to improve search results based on the neurological feedback they receive from brain waves. Thanks to technologies such as Interaxon and Emotiv, EEG devices have readily been made available to researchers interested in neuro-related studies that otherwise would have not had access to expensive fMRI machines. Although the two devices measure different aspects of the brain, nonetheless, EEG devices help researchers conduct neuro-related studies.

I envision my dissertation adding to the body of knowledge of Neuro Information Science in developing search engines that, through wearable computing devices that are able to read brain waves and dimensions of emotions in order to improve search results based on the neurological feedback that the search engines receive from brain waves. In other words, search engines become an extension of the human brain by receiving brain waves that constantly provide neurological feedback in terms of the search results that they provide. All the while, the search engine reads brain waves by receiving the brain signals through wearable computing devices. Gradually, the search engine may ‘learn to improve’ its results based on, for example, alpha (calm) brain waves received.

4        Conclusion

This research topic will be a beneficial addition to the current body of knowledge in the field of Neuro Information Science. We need to increase our body of knowledge and strive to understand how human affective responses impact human-computer-interaction. This, in turn, will help us design smarter information retrieval systems.

Most recently, Artificial Neural Networks, the complex adaptive deep learning systems (a step beyond machine learning) that use statistical learning algorithms, increasingly strive to model the human brain’s biological neuron networks and architecture. These computations, although artificial, strive to model human decision-making processes and aim to estimate a wide range of computational functions based on large sets of data inputs. It is worth noting that artificial neural networks, while quite sophisticated in computing and recognizing patterns, at the moment, primarily receive their input from data types, such as pixel, binary, digital, etc. These artificial neural networks are codes that aim to stimulate the way in which the human brain learns, more specifically in recognizing patterns or creating memories. The codes are organized in layers in order for the systems to learn to understand various data inputs.

While the artificial neural networks are still in their infancy, it is essential to recognize that, to this day and to my knowledge, they are based solely on digital data input. System programmers and architectures fail to approach these efforts based on a holistic view of the human brain. In other words, the main component of emotion is missing from this equation. I propose that adding one additional data input of human emotion may improve these artificial neural networks. One of the main contributions of this research paper is my proposal to the scholars of Artificial Intelligence to include human emotions readings via wearable computing devices as an additional data put for their statistical learning algorithms when creating these artificial neural networks.


Challenges & Limitations: Smart Affective Neuro Search

In my previous post, Smart Affective Neuro Search, partially I discussed my (in progress) dissertation as well as my somewhat unconventional proposal in regards to the implications this model may have in the industry. Here, I intend to examine and discuss some of the challenges and limitations of this model.

In my pilot study, I was able to test my research design along with its proposed methods and measurement techniques. One of these techniques included the Observer Methods, more specifically body movements.

Shortly, I will discuss some of the challenges and limitations of my model. But before that, let’s review the different views when it comes to observing and measuring body movements.

Body Movement – Observer Methods

Some researchers debate whether body movements are valid indicators of human emotions. However, many studies include strong evidence that associates body movements with specific emotions (Wallbott, 1988; de Meijer, 2005). Boone and

Cunningham (1998) were also able to show connection between certain emotions and certain body movements. These findings pertain to the Darwinian theory of discrete emotions, where some body movements directly relate to specific emotional states (Wallbott, 1998). Furthermore, Gunes and Piccardi (2007) identified six facial and body gestures, connecting them with various emotions (see table below).

 Image

Table. List of Bodily Emotions (Gunes & Piccardi, 2007)

 

This table suggests that certain emotions may be assessed by certain body movements. For example, this table suggests that hands resting on the waists or made to fists appear to be indicators of anger in an individual. Moreover, the table indicates that signs of anxiety in participants may be detected through observations of the location of their hands on the table surface.

Moreover, researchers have been monitoring hand movements in hope of establishing correlations between emotions and these hand movements. In these studies, researchers either study glove movements of the users or they have computer programs observe the movements. While glove-based studies analyze the hand gestures using model-based techniques, the computer-based approach observes hands in images using appearance-based techniques (Chen et al., 2003).

Challenges & Limitations

Although human body movements play a big role in emotional communication (Fasel & Luettin, 2003), the complex motor movements involved may contribute to major amount of ‘noise’ when it comes to the readings of brain electrical signals. The current EEG devices in the market today, are partly designed to include noise suppressions. However, they still may not be able to fully suppress all the ‘noise’ emanating from major body movements, such as head or hand movements while participants conduct search tasks on various computer devices.

These, I believe are some of the challenges and limitations when it comes to this proposed model for developing Affective Neuro Search. It is my intention to, through continuous research, address these challenges and limitations.

In future posts, and as I progress in my dissertation, I will discuss proposed ways in which raw EEG data may be best analyzed for the purpose of Affective Neuro Search and such…

 

Please also see here about exciting and emerging wearable computing and AI devices here: Brain controlled airplanesneurogaming, and robots that learn behavior.


Smart (Affective) Neuro Search…

I am humbled by the overwhelming number of views and comments on my previous post, Affective Smart Search. Here, I intend to elaborate on my proposal in regards to the implications that my doctoral dissertation (in progress) may have in the industry.

(Please also see the Disclaimer page.)

Through a pilot study, I was able to test one of my hypotheses that: ‘Aroused dimensions of emotions (high intensity emotions, such as anger) that include high-frequency Beta (and possibly Gamma) brain waves impact search performance (efficiency and effectiveness) negatively.’

Shortly, I will discuss my proposal in regards to the implications that this doctoral dissertation may have in the industry. But before that, let’s review the different views of dimensions of emotions and how they may correlate with high and low frequency brain waves.

Views on Structure of Emotions

There are two main views on the structure of emotion: 1) discrete and 2) continuous approaches. Darwin, the father of the discrete approach, claimed that there exist six basic emotions: fear, happiness, surprise, anger, sadness, and disgust (Darwin, 1872; Ekman, 1992). These theorists argue that these six basic emotions are universal and that humans, regardless of their cultural background, appear to both display and recognize these six distinct emotions. On the other hand, the continuous approach addresses different ‘dimensions’ of emotions (Russel & Mehrabian, 1977; Russel, 1994). These theorists state that there are two dimensions of emotions, valence and arousal (Russell, 1994; Russell & Mehrabian, 1977; Russell & Steiger, 1982; Barrett & Russell, 1999).

While the discrete approach includes the list of discrete emotions, the dimensional self-report approach utilizes dimensions of emotions, arousal and valence (Wundt, 1904). The arousal dimension, as Wundt explains, measures the calmness versus the excitement of an emotion, ranging from calming to exciting (or agitating) states. On the other hand, valence indicates the positivity versus the negativity of an emotion, ranging from highly positive to negative states. As a result, in this method, participants indicate their subjective experience through these two coordinates.

Emotional Dimensions Associated with Brain Waves

While valence assesses the pleasantness (positivity/negativity of an emotion), arousal is explained to represent the intensity of an emotion. Valence (or positive happy emotions) result in a higher frontal coherence in alpha, and higher right parietal beta power, compared to negative emotion Arousal (or excitation) appear to present a higher beta power and coherence in the parietal lobe, plus lower alpha activity.

Russel’s (1989) research shows that the following two emotional dimensions are associated with various brain waves:

  • Theta waves, also seen in meditative states (Cahn & Polich, 2006), show arousal or drowsiness in adults
  • Alpha waves are exhibited when closing the eyes and during relaxation
  • Beta waves, linked with motor behavior, occur when the individual is actively moving (Pfurtscheller and da Silva, 1999). Low beta frequencies are often associated with concentration and/or active thinking
  • Gamma waves represent cognitive or motor functions (Niedermeyer & da Silva, 2004)

Neurophysiologic Methods

Neurophysiologic methods aim to monitor and read human body responses in reaction, such as skin conductance, blood pressure, heart pulse rate, and most recently brain activities, in order to infer human emotional states. Most recently, and the most non-invasive EEG devices, such as Emotiv or Interaxon, are gaining increased respect in the research community.

Industry Implications
And here comes my unconventional ‘out of the box’ proposal… I envision my dissertation add to the body of knowledge of Neuro Information Science in developing search engines that, through wearable computing devices, are able to read human brain waves, and dimensions of emotions thereof, in order to improve search results based on the neurological feedback that the search engines receive from user’s brain waves. In other words, search engines become an extension of the human brain by receiving brain waves that constantly provide neurological feedback in terms of the search results that they provide.

For example, at the time when the search result is being presented on the screen, high frequencies of brain waves may be an indication of high intensity emotions, such as frustration. All the while, the search engines read user brain waves by receiving the brain signals through wearable computing devices. Gradually, the search engine may ‘learn to improve’ its search results based on, for example, alpha (or calmer) brain waves received.

I my future posts, and as I progress in my dissertation, I will elaborate more and will discuss challenges and limitations of this model…

(Read more about exciting and emerging wearable computing and AI devices here: Brain controlled airplanesneurogaming, and robots that learn behavior.)

(Please also see the Disclaimer page.)


‘Smart’ Affective Search: Brain Activities to Help Improve Search Results?

In an era where we are creating brain controlled airplanes, neurogaming, and robots that learn behavior by reading human emotions, there appear to be no limits in having search engines read human emotions in order to improve search results based on the neurological feedback they receive from user’s brain waves. Thanks to companies such as Interaxon and Emotiv, EEG devices have readily been made available to researchers interested in neuro-related studies, who otherwise would have not had access to expensive fMRI machines. Although the two devices measure different entities of the brain, nonetheless, EEG devices help enthusiastic, but low budget, researchers (such as me!) conduct neuro-related studies.

My doctoral research topic aims to examine cognitive relationships between dimensions of human emotions and information retrieval, as in search performance, in the field of neuro information science (Gwizdka, 2012). This study aims to increase our understanding in regards to affective search, improving information systems design practices, and investigating ways to design ‘smart’ information systems that learn and improve search results based on neuro feedback.

To illustrate, emerging expressions, such as “pleasurable engineering” or “emotional design”, have not only become the driving factors in information retrieval system design (Nahl & Bilal, 2007) but also illustrate the important role of emotions in human-computer-interaction. Information retrieval entails complicated cognitive processes, composed of human cognitive processes as well as human physiological and neurological reactions (Picard, 2001). However, our understanding of how emotions affect information retrieval is limited (Nahl & Bilal, 2007), so is our understanding when it comes to the effects of physiological and neurological responses on information retrieval, more specifically on web search performance.

Hence, for us to be able to design better search engines, we need to understand both ‘human-computer-interaction’ as well as ‘brain-computer-interaction’ processes, such that the two not be treated separately.

Keywords: affective information retrieval, affective search, neuro-information science, web search performance, affective information behavior, EEG in information retrieval, emotional design, brain computer interaction


German Researchers Build A Plane Controlled By Your Brain

My dream of further research on mind controlled computing materializes, as I see these types of research results. A great read!


My Doctoral Confirmation of Candidature

Two years into the program and I am having my confirmation of candidature seminar on August 7, 2013!! Both excited and anxious to be defending my confirmation for the school of Information Systems panel and advisory. Here is the abstract, if interested… 

 

Keywords: affective information retrieval, affective search, neuro-information science, web search performance, affective information behavior, EEG in information retrieval, emotional design

ABSTRACT

In the past decade, the affective component of information retrieval system design has increasingly become an essential part of research in information retrieval. Expressions such as “pleasurable engineering” or “emotional design” have become the driving factors in information design, where these expressions have also been extended to information retrieval system design (Nahl & Bilal, 2007). These emerging expressions indicate the important role of emotions in human-computer-interaction.

Information retrieval processes entail complicated cognitive processes. These sophisticated processes are composed of not only human cognitive processes but also human emotion responses (Picard, 2001) where these responses entail physiological as well as neurological reactions. In order to understand the role of affective responses in information retrieval, more specifically within search process, researchers need to investigate these interactions from multiple perspectives (Scherer, 2005).  However, our understanding of how emotions affect information retrieval, as revealed in search performance, is limited (Nahl & Bilal, 2007).

There is a gap in the current body of knowledge on the effect of physiological and neurological emotion responses on information retrieval, more specifically on web search processes and performance. This research aims to examine causal relationship, if any, between dimensions of human emotions and web search performance. Specifically, I intend to contribute to affective web search studies by applying emerging and cutting edge research technologies in the field of neuro-information science (Gwizdka, 2012)—such as electroencephalography (EEG)—thereby increasing our understanding of affective search and improving information systems design practices. By addressing this gap, I intend to make a significant contribution towards the specific fields of affective search and neuro-information science.